Deployment of dynamic vision sensor hybrid element in method for tracking a controller and simultaneous body tracking, slam or safety shutter

ABSTRACT

A position and orientation of a controller are determined from a known configuration of two or more light sources with respect to each other and with respect to a controller body and from output signals from a dynamic vision sensor (DVS) generated in response to changes in light output from the light sources. The output signals indicate times events at corresponding light-sensitive elements in an array in the DVS and array locations of the light-sensitive elements. A position and orientation of one or more objects are determined from signals generated by two or more light-sensitive elements resulting from other light reaching the two or more light-sensitive elements.

FIELD OF THE INVENTION

Aspects of the present disclosure relate to game controller tracking,specifically aspects of the present disclosure relate to game controllertracking using a dynamic vision sensor.

BACKGROUND OF THE INVENTION

Modern Virtual Reality (VR) and Augmented Reality (AR) implementationsrely on accurate and fast motion tracking for user interaction with thedevice. AR and VR often rely on information relating to the location andorientation of a controller relative to other objects. Many VR and ARimplementations rely on a combination of inertial measurements taken byaccelerometers or gyroscopes within a controller and visual detection ofthe controller by an external camera to determine the location andorientation of the controller.

Some of the earliest implementations use infrared lights detected by aninfrared camera with a defined detection radius on a game controllerpointed at a screen. The camera takes images at a moderately fast rateof 200 frames per second and the location of the infrared lights aredetermined. The distance between the infrared lights is predeterminedand from the relative location of the infrared lights in the cameraimage a position of the controller relative to the screen can becalculated. Accelerometers are sometimes also used to provideinformation on relative three-dimensional change in position ororientation of the controller. These prior implementations rely on afixed position of a screen and a controller that is pointed towards thescreen. In modern VR and AR implementations the Screens may be placedclose to a user's face in a head mounted display that moves with theuser. Thus, having an absolute light position (also referred to as alight house) becomes undesirable because the user must set upindependent light house points that require extra set up time and limitthe extent of the user's movement. Additionally, even the moderatelyfast frame rate of the infrared camera at 200 frames per second was notfast enough to provide smooth feedback for motion. Furthermore, thissimplistic set up does not lend itself for use with more moderninside-out detection methods such as room mapping and hand detection.

More recent implementations use a camera and accelerometer inconjunction with trained machine learning algorithms trained to detecthands, controllers and/or other body parts. For smooth motion detectiona high frame rate camera must be used to generate image frames for bodypart/controller detection. This generates a large amount of data thatmust be processed quickly for a smooth update rate. Thus, expensivehardware must be used to process the frame data. Additionally, much ofthe frame data in each of the frames is discarded as unnecessary becauseit is not related to motion tracking.

It is within this context that aspects of the present disclosure arise.

BRIEF DESCRIPTION OF THE DRAWINGS

The teachings of the present invention can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings, in which:

FIG. 1 is a diagram depicting an implementation of game controllertracking using a DVS with a single sensor array according to an aspectof the present disclosure.

FIG. 2 is a diagram depicting an implementation of game controllertracking using a DVS with a dual sensor array according to an aspect ofthe present disclosure.

FIG. 3 is a diagram depicting an implementation of game controllertracking using a combination DVS with a single sensor array and a cameraaccording to an aspect of the present disclosure.

FIG. 4 is a diagram showing DVS tracking movement of a game controllerhaving two or more light sources according to an aspect of the presentdisclosure.

FIG. 5 is a diagram depicting an implementation of head tracking orother device tracking using a controller having DVS with a single sensorarray according to an aspect of the present disclosure.

FIG. 6 is a diagram depicting an implementation of head tracking orother device tracking using a game controller having a DVS with dualsensor arrays according to an aspect of the present disclosure.

FIG. 7 is a diagram showing an implementation of head tracking or otherdevice tracking using a controller having a combination DVS with asingle sensor array and camera according to an aspect of the presentdisclosure.

FIG. 8 is a flow diagram depicting a method for motion tracking with aDVS using one or more light source and a light source configurationfitting model according to an aspect of the present disclosure.

FIG. 9 is a flow diagram showing a method for motion tracking with a DVSusing time stamped light source position information according to anaspect of the present disclosure.

FIG. 10A is a diagram depicting the basic form of an RNN having a layerof nodes each of which is characterized by an activation function, oneinput weight, a recurrent hidden node transition weight, and an outputtransition weight according to aspects of the present disclosure.

FIG. 10B, is a simplified diagram showing that the RNN may be considereda series of nodes having the same activation function moving throughtime according to aspects of the present disclosure.

FIG. 10C depicts an example layout of a convolution neural network suchas a CRNN according to aspects of the present disclosure.

FIG. 10D shows a flow diagram depicting a method for supervised trainingof a machine learning neural network according to aspects of the presentdisclosure.

FIG. 11A is a diagram depicting a hybrid DVS having multiple co-locatedsensor types according to aspects of the present disclosure.

FIG. 11B is a diagram of a hybrid DVS having multiple sensor typesarranged in a checkerboard pattern in the array according to aspects ofthe present disclosure.

FIG. 11C is a cross-sectional schematic diagram of a hybrid DVS havingmultiple sensor types arranged in a pattern in the array according toaspects of the present disclosure.

FIG. 11D is a cross-sectional schematic diagram of a hybrid DVS havingmultiple filter types arranged in a pattern in the array according toaspects of the present disclosure.

FIG. 12 is a diagram showing a hybrid DVS with multiple sensor typeshaving inputs separated with a light separator according to aspects ofthe present disclosure.

FIG. 13 is a diagram depicting a hybrid DVS with multiple sensor typeshaving inputs separated by a microelectromechanical (MEMS) mirroraccording to aspects of the present disclosure.

FIG. 14 is a diagram showing a hybrid DVS with multiple sensor typeshaving inputs filtered temporally according to aspects of the presentdisclosure.

FIG. 15 is a diagram depicting body tracking with a DVS according toaspects of the present disclosure.

FIG. 16A is a diagram depicting a headset with a safety shutter dooraccording to aspects of the present disclosure.

FIG. 16B is a diagram showing a headset with a sliding safety shutteraccording to aspects of the present disclosure.

FIG. 16C is a diagram depicting a headset with a louvered safety shutteraccording to aspects of the present disclosure.

FIG. 16D is a diagram showing a headset with a fabric safety shutteraccording to aspects of the present disclosure.

FIG. 16E is a diagram showing a headset with a liquid crystal safetyshutter according to aspects of the present disclosure.

FIG. 17 is a diagram depicting finger tracking with a DVS and controlleraccording to aspects of the present disclosure.

FIGS. 18A-18B are schematic diagrams illustrating gaze tracking withinthe context of aspects of the present disclosure.

FIG. 19 is a block system diagram for a system for tracking with a DVSaccording to aspects of the present disclosure.

DESCRIPTION OF THE SPECIFIC EMBODIMENTS

Although the following detailed description contains many specificdetails for the purposes of illustration, anyone of ordinary skill inthe art will appreciate that many variations and alterations to thefollowing details are within the scope of the invention. Accordingly,the exemplary embodiments of the invention described below are set forthwithout any loss of generality to, and without imposing limitationsupon, the claimed invention.

Introduction

A new type of vision system has recently been developed called, aDynamic Vision System (DVS) the DVS utilizes only the change in lightintensity of an array of light sensitive pixels to resolve changes in ascene. The DVS has an extremely fast update rate and instead ofdelivering a stream of image frames, the DVS provides a near continuousstream of locations of changes in pixel intensity. Each change in pixelintensity may be called an Event. This has the added benefit of greatlyreducing the extraneous data output.

Two or more light sources may provide continuous updates as to thelocation of the DVS camera in relation to the position indicator lightsat an update rate determined by the speed of flashing of the lights. Insome implementations the two or more light sources may be infrared lightsources and the DVS may use infrared light sensitive pixels.Alternatively, the DVS maybe sensitive to the visible light spectrum andthe two or more light sources may be visible light sources or visiblelight at a known wavelength. In implementations having a DVS sensitiveto visible light, the DVS may also be sensitive to motion occurringwithin its field of view (FOV). The DVS may detect changes in lightintensity caused by reflection of light off of the moving surface. Inimplementations using an Infrared sensitive DVS an infrared illuminatorlight may be used to detect movement in the FOV through reflection.

Implementations

FIG. 1 depicts an example of an implementation of game controllertracking using a DVS 101 with a single sensor array according to anaspect of the present disclosure. In the implementation shown the DVS ismounted to a headset 102, which may be part of a head mounted display. Acontroller 103 including two or more light sources is within the fieldof view of the DVS 101. In the example shown, the controller 103includes four light sources 104, 105, 106, and 107. These light sourceshave a known configuration with respect to each other and with respectto the controller 103. Here, there is one DVS with a single lightsensitive array. As such, four light sources may be used to accuratelydetermine the position and orientation of the controller 103 relative tothe DVS 101. The known information about the light sources may includethe distance between each of the light sources with respect to each ofthe other light sources and the location of each of the light sources onthe controller 103. As shown the three light sources 104, 105, 106 maydescribe a plane and a light source 107 may be out of plane with respectto the plane described by the three light sources 104, 105, 106. Thelight sources here, have a known configuration of, for example andwithout limitation, a first light source 104 is located on a top frontleft side and second light source 105 is located on a top front rightside, a third light source 106 is located on a top back left side and afourth light source 107 is located on a bottom, front middle of thecontroller. With four light sources a DVS having a single lightsensitive array may be able to determine movements of the controller inthe X, Y, and Z axis. Additionally, an inertial measurement unit (IMU)108 may be coupled to the controller 103. By way of example, the IMU 108may include an accelerometer configured to measure acceleration withrespect to one, two, or three axes. Alternatively, the IMU may include agyroscope configured to sense changes in rotation with respect to one,two, or three axes. In some implementations, the IMU may include both anaccelerometer and a gyroscope. The IMU 108 may be used to refinemovement, position, and orientation determination with the informationfrom the DVS 101 using a processor. The processor may be located in theheadset 102, a game console or other computing device (not shown). TheDVS 101, headset 102, and IMU 108 may be operably coupled to a processor110, which may be located on the headset 102, the controller, 103 or aseparate device, such as a personal computer, laptop computer, tabletcomputer, smartphone, or gaming console. The processor may implementtracking as described herein, e.g., as discussed below with respect toFIG. 4 , FIG. 8 and FIG. 9 . In addition, the processor 110 may controlthe flashing of the light sources 104, 105, 106.

During operation, the DVS 101 having a light sensitive array may detectmovement of the light sources 104, 105, 106, 107 with the lightsensitive array, the change in light detected by the light sensitivearray may be sent to the processor. In some implementations the lightsources may be configured to turn on and off in a predetermined patternwith, for example and without limitation circuitry and/or signals fromthe processor. The predetermined pattern may be used by the processor todetermine the identity of each light source. The identity of the lightsource may include a known location with respect to the controller andwith respect to the other light sources. In other implementations eachlight source may be configured to turn off and on in a predeterminedpattern and that pattern may be used to determine the identity of thatspecific light source. In some implementations the processor may fit aknown configuration of the light sources with respect to the controllerto events detected by the light sensitive array.

The DVS may have a near continuous update rate which can be discretelyapproximated to about 1 million updates per second. The DVS with itshigh update rate may be able to resolve the extremely fast flashingpatterns of the light sources. The flashing rate is limited mainly bythe Nyquist frequency, i.e., half the sample rate of the DVS. The lightsources may be flashed with a duty cycle suitable for detection of theflashes by the DVS. Generally speaking, the “on” time for the flashesshould be sufficiently long that they can be consistently detected bythe DVS. Additionally, small differences in flashing rates may bedetectable due to the high update rate.

The light sources 104, 105, 106, 107 may be broad visible spectrumlights such as incandescent lights or white Light Emitting Diodes.Alternatively, the light sources 104, 105, 106, 107 may be infraredlights or the light sources may have a specific light spectra profiledetectable by the DVS 101. The DVS 101 may include a light sensitivearray that is configured to detect the emission spectra of the lightsources 104, 105, 106, 107. For example, and without limitation, if thelight sources are infrared lights, the light sensitive array of the DVSmay be sensitive to infrared light or if the light sources have aspecific emission spectrum, the light sensitive array may be configuredto have increased sensitivity to the specific emission spectra of thelight source. Additionally for example and without limitation the lightsensitive array of the DVS may be insensitive or exclude otherwavelengths of light not emitted by the light sources e.g., the lightsensitive array may be configured only to detect infrared light if thelight sources are infrared lights.

FIG. 2 illustrates an example of an implementation of game controllertracking using a DVS with a dual sensor array according to an aspect ofthe present disclosure. In this implementation the headset 203 includesa first DVS 201 and a second DVS 202. Alternatively, the headset 203 mayinclude a DVS with a first light sensitive array 201 and a second lightsensitive array 202. The general function of the light sources and DVSis similar as to as described above with respect to FIG. 1 . Theinformation from the second DVS or Second array may be combined withinformation from the first array to provide a better fit for controllerorientation and some depth information. The two DVS or light sensitivearrays may have fields of view that partially overlap allowing the useof binocular parallax.

The two DVS or two light sensitive arrays provide binocular vision fordepth sensing. This further allows for a reduction in the number oflight sources. The first DVS and second DVS or first light sensitivearray and second light sensitive array may be separated by a knowndistance, for example and without limitation around 50-100 millimetersor greater than 100 millimeters. More generally, the separation is largeenough to provide sufficient parallax for a desired depth sensitivitybut not so large so that there is no overlap between the fields of view.As shown, the controller 207 may include a first light source 204, asecond light source 205 and a third light source 206. A fourth lightsource coupled to the controller may not be necessary as the informationfrom the two DVS or two arrays provide enough information fordetermination of the position and orientation of the controller. Thecontroller may include an IMU 208 which may provide additional inertiainformation used to refine the position and orientation determination.

The first DVS 201, second DVS 202 headset 203, and IMU 208 may beoperably coupled to a processor 210, which may be located on the headset203, the controller 207 or a separate device, such as a personalcomputer, laptop computer, tablet computer, smartphone, or gamingconsole. The processor may implement tracking as described herein, e.g.,as discussed below with respect to FIG. 4 , FIG. 8 and FIG. 9 . Inaddition, the processor 210 may control the flashing of the lightsources 204, 205, 206.

While FIG. 2 shows two DVS or two light sensitive arrays aspects of thepresent disclosure are not so limited. The device may include any numberof DVS or light sensitive arrays. For example, and without limitationthree DVS or a DVS having three separated light sensitive arrays mayallow for the use only two light sources coupled with the controller.The third DVS or light sensitive array may be non-collinear with theother two DVS or arrays. Similar to the binocular parallax eachadditional DVS or light sensitive array may be separated by a knowndistance and have fields that partially overlap allowing for greater useof parallax effects. Additionally, some implementations may includemultiple DVS each having multiple light sensitive arrays. For example,and without limitation there may be two DVS each having two separatelight sensitive arrays.

FIG. 3 depicts an example of an implementation of game controllertracking using a combination DVS with a single sensor array and a cameraaccording to an aspect of the present disclosure. In this implementationthe DVS 301 is supplemented with a camera 302. The DVS 301 and camera302 may be coupled to a headset 303. The DVS 301 and camera 302 may havea partially overlapping field of view or share the same field of view.The controller may include three or more light sources 304, 305, 306 ona controller 307. The DVS 301 and camera 302 may be used together todetermine the position and orientation of the controller. Frames fromthe camera 302 may be interpolated using events from the DVS 301. Imageframes may also be used to perform simultaneous localization and mappingto improve controller orientation and position determination.Additionally, image frames from the camera may be used to perform insideout tracking of the user using a machine learning algorithm, for examplehand tracking or foot tracking. An IMU 308 may provide additionalinertial information used to further refine the determination of theposition and orientation of the controller.

The first DVS 301, second DVS 302 headset 303, and IMU 308 may beoperably coupled to a processor 310, which may be located on the headset303, the controller 307 or a separate device, such as a personalcomputer, laptop computer, tablet computer, smartphone, or gamingconsole. The processor may implement tracking as described herein, e.g.,as discussed below with respect to FIG. 4 , FIG. 8 and FIG. 9 . Inaddition, the processor 310 may control the flashing of the lightsources 304, 305, 306.

FIG. 5 is depicts an example of an implementation of head tracking orother device tracking using a controller having a DVS with a singlesensor array according to an aspect of the present disclosure. Here, aDVS 507 is mounted to a controller 508. A headset 501 that is within afield of view of the DVS 507 is tracked using light sources attached tothe headset. The headset 501 may include two or more light sources, herefour light sources 502, 503, 504 and 505. Four light sources provideaccurate information for determination of position in three dimensionswith a single DVS. A smaller number of light sources may be used withadditional DVS or cameras. The position of each light source relative tothe headset 501 may be known to the system and of some importance toprovide relevant information to the DVS. Here, light sources 502, 503and 504 describe a plane; light source 505 is located out of plane ofthe other light sources 502, 503 and 504. This facilitates detection oflocation, orientation, or movement of the headset in three dimensions.The headset 501 may include an IMU 506 which may be to improve positionand orientation estimation with information from the DVS 507.Additionally, the controller 508 may include an IMU 509. The informationfrom the controller IMU 509 may be used to further refine the positionand orientation determination of the headset, for example and withoutlimitation the IMU information may be used to determine if thecontroller is moving relative to the headset and velocity oracceleration of that movement.

The headset 501, IMU 506, DVS 507, controller IMU 509 may be operablycoupled to a processor 510, which may be located on the headset 501, thecontroller 508 or a separate device, such as a personal computer, laptopcomputer, tablet computer, smartphone, or gaming console. The processormay implement tracking as described herein, e.g., as discussed belowwith respect to FIG. 4 , FIG. 8 and FIG. 9 . In addition, the processor510 may control the flashing of the light sources 502, 503, 504, 505.

FIG. 6 illustrates an example of an implementation of head tracking orother device tracking using a game controller having a DVS with dualsensor arrays according to an aspect of the present disclosure. Here thecontroller 605 is coupled to two DVS 606, 607 or a single DVS having twolight sensitive arrays 606 and 607. As discussed above the two DVS ortwo light sensitive arrays may be separated by a suitable distance,e.g., between 500 and 1000 millimeters, and have partially overlappingfields of view. This allows for use of the parallax effect for depthdetermination. Additionally, the headset 601 may include three lightsources 602, 603, 604 and an IMU 608. The use of two DVS or twoseparated arrays 606, 607 may allow the use of less than four lightsources, for example and without limitation three light sources. The twolight sources 602, 603 may describe a line and the third light source604 may be out of line with the other two light sources 602, 603.Information from an IMU 605 coupled to the headset 601 may be used torefine the position and orientation determination.

The headset 601, DVS 606, DVS 607, and IMU 608 may be operably coupledto a processor 610, which may be located on the headset 601, thecontroller 605 or a separate device, such as a personal computer, laptopcomputer, tablet computer, smartphone, or gaming console. The processormay implement tracking as described herein, e.g., as discussed belowwith respect to FIG. 4 , FIG. 8 and FIG. 9 . In addition, the processor610 may control the flashing of the light sources 602, 603, 604.

FIG. 7 depicts an example of an implementation of head tracking or otherdevice tracking using a controller having a combination DVS with asingle sensor array and an image camera according to an aspect of thepresent disclosure. Here a DVS 707 and a camera 708 are coupled to acontroller 706. The camera 708 and the DVS 707 may have partiallyoverlapping fields of view or may have entirely overlapping fields ofview. In some implementations camera pixels and DVS light sensitiveelements may share the same light-sensitive array thereby acting as anintegrated DVS and Camera.

Three or more light sources 702, 703, 704 may be coupled to the headset701. For example, and without limitation the light sources may beintegrated into the headset housing, each light source may be an LED,incandescent, halogen or florescent light emitter mounted to a circuitboard within the headset housing or on the headset housing. In someimplementations a single light emitter may create multiple light sourcesusing plastic or glass light piping or optical fiber that splits lightfrom the single emitter into two or more light sources on the headsethousing.

The three or more light sources 702, 703, 704 may be configured to turnon and off in response to electronic signals. In some implementationsthe three or more light sources may turn on and off in a predeterminedsequence. Each time the light sources 702, 703, 704 move or flash, theDVS 707 may generate an event. The camera 708 generates image frames ofits field of view at a set frame rate. The high update rate DVS mayallow image frames generated by the camera to be interpolated with DVSevents.

In some implementations, an IMU 705 may also be coupled to the headset701. The headset 701, IMU 705, DVS 707, camera 708, and IMU 608 may beoperably coupled to a processor 710, which may be located on the headset701, the controller 706 or a separate device, such as a personalcomputer, laptop computer, tablet computer, smartphone, or gamingconsole. The processor may implement tracking as described herein, e.g.,as discussed below with respect to FIG. 4 , FIG. 8 and FIG. 9 . Inaddition, the processor 710 may control the flashing of the lightsources 702, 703, 704.

Additionally in some implementations the cameras 708 may be a depthcamera such as a depth time of flight (DTOF) sensor. DToF camerasacquire depth images by measuring the time it takes the light to travelfrom a light source to objects in a scene and back to a pixel array. Byway of example, and not by way of limitation, a DToF camera may operateusing continuous wave (CW) modulation, which is an example of anindirect time of flight (ToF) sensing method. In a CW ToF camera, thelight from an amplitude modulated light source is backscattered byobjects in the camera's field of view (FOV), and the phase shift betweenthe emitted waveform and the reflected waveform is measured. Bymeasuring the phase shift at multiple modulation frequencies, one cancalculate a depth value for each pixel. The phase shift is obtained bymeasuring the correlation between the emitted waveform and the receivedwaveform at different relative delays using in-pixel photon mixingdemodulation.

A DTOF system generally includes an illumination module and an imagingmodule. The illumination module consists of a light source, a driverthat drives the light source at a high modulation frequency, and adiffuser that projects the optical beam from the light source to adesigned field of illumination (FOI). The DToF illumination module mayinclude one or more light sources, which may be for example and withoutlimitation an amplitude modulated light emitter such as a verticalcavity surface emitting laser (VCSEL) or edge emitting laser (EEL). Theimaging module may include an imaging lens assembly, band-pass filter(BPF), microlens array and an array of light-sensitive elements thatconvert incident photon energy to electronic signals. The microlensarray increases the amount of light that reaches the light-sensitiveelements and the BPF reduces the amount of ambient light that reachesthe light-elements and the microlens array.

Operation

FIG. 4 shows DVS tracking movement of a game controller having two ormore light sources according to an aspect of the present disclosure. ADVS 401 has the controller 402 within its field of view. As shown, thecontroller 402 includes multiple light sources coupled to the controllerbody. The multiple light sources may be configured turn off and on againat a predetermined rate. Each flash of a light source within the fieldof view of the DVS 401 may generate one or multiple events 403 at theDVS. In the event 403 shown the light sources have all changed from anoff state to an on state thus the event shows all of the light sourcesin an on state. Alternatively, depending on the sensitivity of the DVS,a change in the brightness of a light source may be sufficient totrigger an event 403. It should be noted that in other implementationsthe lights may turn off and on at different rates or at different timesand therefore each event may correspond to less than all of the sourcesbeing lit. The times of the events generated by the flashing lights andtheir corresponding locations within the array may be recorded in amemory (not shown). In some implementations a position and orientationof the controller 402 may be reconstructed from one or more events fromthe DVS.

In some implementations each light source may flash in a pre-determinedtime sequence. The DVS may output a time each event occurred along witheach event as, for example and without limitation, a time stamp. Thetime the event occurred may be used with the predetermined time sequenceto determine the identity of each light in the event e.g., which eventcorresponds to which light source location. In the example shown the oneor more events 403 output by the DVS 401 show a first light 406, secondlight 407, third light 408, and fourth light 409, detected by the lightsensitive array. As discussed above, the identity of each light sourcemay be determined from information output by the DVS and thepredetermined flash sequence of the light sources. For example, andwithout limitation a light sensitive array of the DVS 401 may detect alight event 403 at time T+1, the predetermined sequence may provide thatlight source 406 is turned on at T+1, thus it is determined that thelight event corresponds to light source 406. The predetermined sequencemay be stored in a memory, for example as a table listing sequencetiming and location of each light source. In some implementations thepredetermined sequence may be encoded in the flashing of the lightsthemselves for example and without limitation each light may flash in asequence indicating its identity. For example, a light source labeled 1may blink in a Morse code sequence indicating the number one. Theidentity of the light event may then be recovered through analysis oflight events. Alternatively, sequence information may come from thelight source itself or driver of the light source indicating when thelight source is on or off. Alternatively, the light sources may turn onand off simultaneously and a machine learning algorithm may then beapplied to the detected light events 403 to fit a controller pose 402 tothe events and the known configuration of the light sources.

Additionally, information from the events such as size, intensity andseparation of the lights may be used to determine orientation andposition of the light sources. The system may have information definingeach light source's size, position on the controller body and intensity.From the differences between the detected size, intensity andseparation; position and orientation may be determined with increasedaccuracy. Additionally, if one or more additional DVS or light sensitivearrays are present, parallax information may be used to further enhancethe position and orientation determination.

During operation, the user may change the position and orientation ofthe controller 410. The relatively high update rate of the DVS may allowit to capture a sequence of events 411 as the light events move 412during the change in position and orientation. The movement of the lightevents here is represented in FIG. 4 with vector arrows 412. As thedetected light events move the determined position and orientation ofthe controller 410 may be updated or the determined position andorientation may be updated at a regular interval. The locations andtimes of events detected by the light sensitive elements of the DVS dueto flashing of a first light 415, second light 416, third light 417, andfourth light 418 may be fit to a new position and orientation of thecontroller. Additionally inertial information from an IMU may be used torefine the movement and position and orientation of the controller 410.

The flow diagram shown in FIG. 8 depicts a method for motion trackingwith a DVS 801 using one or more light source and a light sourceconfiguration fitting model according to an aspect of the presentdisclosure. In this implementation the light sources (depicted as LEDs)may turn on and off simultaneously, independently or in a predeterminedsequence. Movement of the light sources or flashing of one or more ofthe light sources generates an event 802 from the DVS 801. Generally, anevent includes electronic signals that relay the following information:a time interval within which the event occurred, a location within thearray of light sensing elements of the DVS 801 where the event occurred,and binary data (e.g., 1 or 0) corresponding to a change of lightintensity greater than some detection threshold. Each event may beprocessed at 803 to format the event into a usable form such as, withoutlimitation, placing event locations within a data array, associatingtime stamps with events, and aggregating multiple events. As an exampleof aggregating events, all events occurring individual elements in thearray within a predefined time interval may be combined into a singledata structure for analysis.

The processed events may then be analyzed to associate the detected DVSevents with corresponding LED pulses 804. In some implementations eachlight source may be turned off and on with a unique predefined timeinterval and thus the time stamps of the aggregated events may be usedto determine the predefined time interval from events to associate aparticular light source to particular events. For example, and withoutlimitation a spatial pattern of aggregated events occurring within thepredefined time interval or sequence of time intervals may be analyzedto determine whether the pattern is consistent with pulsing of an LED.Event patterns that are too big or too small or too irregular in shapemay be excluded as LED events. Additionally, timing of events may alsobe analyzed; events that are too short or too long may also be excluded.

A trained machine learning model 805 may be applied to the processedevents. The model 805 may include information about the configuration ofthe light sources such as the size of the light sources and theirrelative locations with respect to the controller body. The machinelearning model may be trained with training event data havingcorresponding masked positions and orientations of a controller as willbe discussed in a later section. The trained machine learning model isapplied to the processed event data to determine a correspondence 806between the detected pulses 804 and a pose 808. The trained machinelearning model may fit a pose 808, e.g., position and orientation, ofthe controller to the one or more processed events, e.g., detected LEDpulses 804. Alternatively, a fitting algorithm may be applied to theprocessed events instead of the trained model 805. The fitting algorithmmay use a hand developed model of the light sources to fit a positionand orientation of the controller to the processed events.Alternatively, the fitting algorithm may be a hypothesis and test typealgorithm which tries all the possible permutations of lightcorrespondences, and finds the best fitting use redundant light sources.After that a tracking/prediction algorithm can be applied to keeptracking the light sources. Additionally, the predicted current pose maybe used to predict the next pose 809. Inertial data from the IMU 807 maybe fused 810 with the predicted pose 808 to generate the final predictedposition and orientation of the controller. The fusion may be performedby a trained machine learning algorithm, trained to refine controllerposition and orientation using inertial data. Alternatively, the fusionmay be performed by for example and without limitation a Kalman filter,or nonlinear optimization.

FIG. 9 illustrates a method for motion tracking with a DVS using timestamped light source position information according to an aspect of thepresent disclosure. In this implementation times of the events output byDVS 901 are used to determine position and orientation of thecontroller. Here, the light sources are depicted as LEDs and each LEDmay turn on at a different time as depicted by the graphs. Each time anLED turns on or off, a DVS having the LEDs within its field of view maygenerate an event 902. Each event may include electronic signalscorresponding to information such as a time interval within which theevent occurred, a location within the array of light sensing elements ofthe DVS 901 where the event occurred, and binary data (e.g., 1 or 0)corresponding to a change of light intensity greater than some detectionthreshold. Each event may be processed 903 to format the event into ausable form such as, without limitation placing event locations within adata array and aggregating multiple events, e.g., by combining multipleevents occurring at individual elements in the array within a predefinedtime interval into a single data structure, as discussed above. Theprocessed events may then be analyzed to detect corresponding LED pulses904. For example, and without limitation the shape and size of eventsmay be analyze for regularity and fit of light sources, events that aretoo big or too small or too irregular in shape may be excluded as LEDevents, noise suppression such as event averaging may be perform toremove random events. Additionally, timing of events may also beanalyzed; events that are too short or too long may also be excluded, ormultiple events may be condensed into a single event by eliminatingevents that are collocated with an initial event and close in time butafter an initial event.

Once events are processed, individual LED position may be determined905. Determining the individual LED position may be performed by usingthe time sequence that the LEDs turn on and off. For example and withoutlimitation, a time of an event or events may be compared to a known timesequence of LED flashes. The known time sequence may be for example atable having LED on and off times and locations on the controller bodyfor each LED, or time stamps from an LED driver for when each LED is onor off. If timestamps are used, the timestamps may be correlated withLED locations on the controller body. From the timing sequence, LEDlocation information and processed event information, a matchingposition and orientation of the controller may be determined. IMU data907 may be combined with the previously determined LED locations and theinertial data from the IMU through a Kalman filter 908. The Kalmanfilter may predict the location of the light sources based on theinertial information from the IMU, this prediction may be combined withposition information determined from the LED time sequence to refinemovement data and produce a final pose 909 and refine future estimates.

General Neural Network Training

According to aspects of the present disclosure, the tracking system mayuse machine learning with neural networks (NN). For example, the trainedmodel 805 discussed above may use machine learning as discussed below.The machine learning algorithm may use a training data set, which mayinclude inputs from the DVS such as events or processed events withknown controller positions and orientations as labeling. Additionally,machine learning algorithms using NNs may perform fusion betweencontroller position and orientation determined from DVS information andinertial information from the IMU. The training set for fusion may forexample and without limitation be potential controller positions andorientations and inertial data with final positions and orientations. Insome implementations a machine learning algorithm may be trained toperform simultaneous localization and mapping (SLAM) with a training setwith objects such as the ground, landmarks and body parts with hiddenlabelings. The hidden labeling may include the identity of the objectsand their relative location. As is generally understood by those skilledin the art, SLAM techniques general solve the problem of constructing orupdating a map of an unknown environment while simultaneously keepingtrack of an agent's location within it.

The NNs may include one or more of several different types of neuralnetworks and may have many different layers. By way of example and notby way of limitation the neural network may consist of one or multipleconvolutional neural networks (CNN), recurrent neural networks (RNN)and/or dynamic neural networks (DNN). The Motion Decision Neural Networkmay be trained using the general training method disclosed herein.

By of example, and not limitation, FIG. 10A depicts the basic form of anRNN that may be used, e.g., in the trained model 805. In the illustratedexample, the RNN has a layer of nodes 1020, each of which ischaracterized by an activation function S, one input weight U, arecurrent hidden node transition weight W, and an output transitionweight V. The activation function S may be any non-linear function knownin the art and is not limited to the (hyperbolic tangent (tanh)function. For example, the activation function S may be a Sigmoid orReLu function. Unlike other types of neural networks, RNNs have one setof activation functions and weights for the entire layer. As shown inFIG. 10B, the RNN may be considered as a series of nodes 1020 having thesame activation function moving through time T and T+1. Thus, the RNNmaintains historical information by feeding the result from a previoustime T to a current time T+1.

In some implementations, a convolutional RNN may be used. Another typeof RNN that may be used is a Long Short-Term Memory (LSTM) NeuralNetwork which adds a memory block in a RNN node with input gateactivation function, output gate activation function and forget gateactivation function resulting in a gating memory that allows the networkto retain some information for a longer period of time as described byHochreiter & Schmidhuber “Long Short-term memory” Neural Computation9(8):1735-1780 (1997), which is incorporated herein by reference.

FIG. 10C depicts an example layout of a convolution neural network suchas a CRNN, which may be used, e.g., in the trained model 805 accordingto aspects of the present disclosure. In this depiction, the convolutionneural network is generated for an input 1032 with a size of 4 units inheight and 4 units in width giving a total area of 16 units. Thedepicted convolutional neural network has a filter 1033 size of 2 unitsin height and 2 units in width with a skip value of 1 and a channel 136of size 9. For clarity in FIG. 10C only the connections 1034 between thefirst column of channels and their filter windows is depicted. Aspectsof the present disclosure, however, are not limited to suchimplementations. According to aspects of the present disclosure, theconvolutional neural network may have any number of additional neuralnetwork node layers 1031 and may include such layer types as additionalconvolutional layers, fully connected layers, pooling layers, maxpooling layers, local contrast normalization layers, etc. of any size.

As seen in FIG. 10D Training a neural network (NN) begins withinitialization of the weights of the NN at 1041. In general, the initialweights should be distributed randomly. For example, an NN with a tanhactivation function should have random values distributed between

${- \frac{1}{\sqrt{n}}}{and}\frac{1}{\sqrt{n}}$

where n is the number of inputs to the node.

After initialization, the activation function and optimizer are defined.The NN is then provided with a feature vector or input dataset at 1042.Each of the different feature vectors may be generated by the NN frominputs that have known labels. Similarly, the NN may be provided withfeature vectors that correspond to inputs having known labeling orclassification. The NN then predicts a label or classification for thefeature or input at 1043. The predicted label or class is compared tothe known label or class (also known as ground truth) and a lossfunction measures the total error between the predictions and groundtruth over all the training samples at 1044. By way of example and notby way of limitation the loss function may be a cross entropy lossfunction, quadratic cost, triplet contrastive function, exponentialcost, etc. Multiple different loss functions may be used depending onthe purpose. By way of example and not by way of limitation, fortraining classifiers a cross entropy loss function may be used whereasfor learning pre-trained embedding a triplet contrastive function may beemployed. The NN is then optimized and trained, using the result of theloss function and using known methods of training for neural networkssuch as backpropagation with adaptive gradient descent etc., asindicated at 1045. In each training epoch, the optimizer tries to choosethe model parameters (i.e., weights) that minimize the training lossfunction (i.e., total error). Data is partitioned into training,validation, and test samples.

During training, the Optimizer minimizes the loss function on thetraining samples. After each training epoch, the model is evaluated onthe validation sample by computing the validation loss and accuracy. Ifthere is no significant change, training can be stopped and theresulting trained model may be used to predict the labels of the testdata.

Thus, the neural network may be trained from inputs having known labelsor classifications to identify and classify those inputs. Similarly, aNN may be trained using the described method to generate a featurevector from inputs having a known label or classification. While theabove discussion is relation to RNNs and CRNNS the discussions may beapplied to NNs that do not include Recurrent or hidden layers.

Hybrid Sensor

FIG. 11A is a diagram depicting a hybrid DVS having multiple co-locatedsensor types according to aspects of the present disclosure. In someimplementations a hybrid DVS may be used to combine multiple sensortypes into one device. The multiple sensor types may be for example andwithout limitation DVS infra-red light sensitive elements, DVS visiblelight sensitive elements, DVS wavelength specific light sensitiveelements, visible light Camera pixels, Infra-red Camera pixels, DTOFCamera pixels. A first sensor type 1102 may be interspersed with asecond sensor type 1102 on the same array 1101. For example, and withoutlimitation DVS visible light sensitive elements 1103 may surround avisible light camera pixel 1102, or DVS infrared sensitive elements 1103may surround a DVS visible light sensitive element 1102 or visible lightcamera pixels 1103 may surround DVS infra-red sensitive elements 1102 orany combination thereof. While a single element 1102 is shown surroundedby other elements 1103 aspects of the present disclosure are not solimited. The single element may comprise a cluster of multiple DVS lightsensitive elements or camera pixels for example and without limitation acluster of four camera pixels may be surrounded by eight DVS lightsensitive elements or one camera pixel may be surrounded by eight pairs,triplets or quadruplets of DVS light sensitive elements.

Additionally, as shown in FIG. 11B a hybrid DVS may have multiple sensortypes arranged in a checkerboard pattern in the array. Here blocks of afirst sensor type 1102 are evenly distributed with blocks of a secondsensor type 1103. Each of the first sensor type 1102 and second sensortype 1103 may be different. For example, and without limitation thefirst sensor type 1102 may be an DVS infra-red light sensitive elementand the second sensor type 1103 may be a DVS visible light sensitiveelement.

Alternatively, differentiation of sensor types may be performed byfiltering. In these implementations one or more filters selectivelytransmit light to light sensitive element located behind the one or morefilters. The one or more filters may selectively transmit, for exampleand without limitation, a certain wavelength or wavelengths of light ora certain light polarization. The one or more filters may alsoselectively block a certain wavelength or wavelengths of light or acertain light polarization. The light sensitive elements behind the oneor more filters may be configured for use as different sensor types. Forexample, and without limitation an infrared pass filter, which allowsonly infrared light to pass 1102 may cover one or more sensor elementsin the array 1101 and other sensor elements may be unfiltered or may beinfra-red cut filters 1103. In another alternative implementation theone or more filters may for example and without limitation be an opticalnotch filter allowing only a certain wavelength of light to pass 1102while the other filters may block that particular wavelength whileallowing others to pass 1103. This filtering may allow use ofwavelengths for illuminator light for DTOF and specific wavelengths forDVS light sensitive elements thus reducing the likelihood of erroneouslight source detection. Here the sensor elements may be DVS lightsensitive elements of any type or camera pixels of any type.

Patterned sensor or filter elements may be incorporated into hybridimaging units, e.g., as shown in FIG. 11C and FIG. 11D. FIG. 11Cillustrates one example of a hybrid DVS imaging unit 1112C having one ormore lens elements 1114, an optional microlens array 1116, a bandpassfilter 1118 and a patterned hybrid sensor array 1120. The sensor arrayincludes DVS sensor elements 1122 and conventional imaging sensorelements 1124, which may be arranged in a pattern, e.g., as depicted inFIG. 11A or FIG. 11B. Such imaging units may be used in conjunction withillumination units (not shown) in a DTOF sensor. The hybrid DVS imagingunit 1112D shown in FIG. 11D has a DVS sensor array 1126 and patternedfilter element 1118 with bandpass regions 1118A and band cut regions1118B arranged in a pattern, e.g., as depicted in FIG. 11A or FIG. 11B.

FIG. 12 is a diagram showing a hybrid DVS with multiple sensor typeshaving inputs separated with a light separator according to aspects ofthe present disclosure. In this implementation the light separator 1204may filter light based on wavelength; the array 1201 may includemultiple sensor element types physically separated based on thewavelength or polarization of light desired to be detected. For example,and without limitation unpolarized white light 1205 (which is a mixtureof at least all visible wavelengths of light and in most cases includessome infrared wavelengths) may enter the light separator 1204, which maybe a dispersing prism, diffraction grating, or dichroic mirror or thelike. As shown the white light 1205 entering the light separator 1204may be separated by wavelength or polarization with some light 1206incident to a first portion of the array 1202 while other light 1207 isincident to a second portion of the array 1203. Here, the lightseparator 1204 may be thought of as a filter which changes the angle ofdiffraction based on wavelength or polarization. While the array showndepicts the array as a single unit with a hard separation line 1203,aspects of the present disclosure are not so limited. A first portion ofthe array 1201 may have up to a millimeter of separation between thesecond portion of the array 1202 and though the arrays are shown asseparated top from bottom, other implementations may have horizontally,diagonally or circumferentially separated portions. Additionally, thelight separator here may be combined with different filtering ordifferent sensor configurations, e.g., as shown in FIGS. 11A and 11B toprovide additional light wavelength separation for different sensortypes.

FIG. 13 is a diagram depicting a hybrid DVS with multiple sensor typeshaving inputs separated by a microelectromechanical (MEMS) mirroraccording to aspects of the present disclosure. Here a MEMS mirror 1304may oscillate between different positions at set times to reflect lightto a first portion 1301 or a second portion 1302 of the array dependingon the time the light 1305 arrives at the MEMS mirror 1304. The firstportion 1301 and second portion 1302 of the array may be physicallyseparated 1303 from one another based on the incident angle of the lightreflected by the MEMS mirror 1304. In this way light may be temporallyfiltered between the different sensor types. Such temporal filtering maybe timed in accordance with a known pattern of flashing of light sourceson a controller or headset. For example and without limitation, thelight sources on a controller or headset may be turned on for apredetermined duration at a certain interval. For example the lightsources may be turned on for 60 microseconds every 100 microseconds. Insuch a case, the MEMS mirror 1304 may be synchronized to reflect light1305 to a DVS portion of the array 1301 for greater than 60 microsecondsevery 100 microseconds or less to capture changes in the light sources.The other reflected light 1307 is detected by the second portion of thearray 1302 during times when the light sources are off and may captureambient light for image tracking or DTOF.

Alternatively, the MEMS mirror 1304 may filter light 1305 based onwavelength. The MEMS mirror in these implementations may be, for exampleand without limitation a MEMS Fabry-Perot filter or diffraction grating.The MEMS mirror may diffract light of a first wavelength range 1306 toat least a first portion 1301 of the array or diffract light of a secondwavelength range 1307 to a second portion of the array 1302.

FIG. 14 is a diagram showing a hybrid DVS with multiple sensor typeshaving inputs filtered temporally according to aspects of the presentdisclosure. Here, a filter selectively allows light to pass to the arraybased on time. In some implementations the filter may be an opticalwaveguide. At a first time step the filter may allow a first wavelengthor polarization of light through to the array 1401 and may block asecond wavelength or polarization of light or other wavelengths orpolarizations. At a second time step the filter may allow a secondwavelength or polarization 1402 but block the first wavelength orpolarization of light or other wavelengths polarizations. In this waylight may be temporally filtered which may be useful for tracking withdifferent sensor types. For example and without limitation, the lightsources coupled to the controller or headset may be infrared light orhave a specific wavelength. The light sources may be configured to turnon and off at a particular interval. The particular interval at whichthe light sources turn on and off may be a sequence or a coded pattern.The temporal filtering may activate to allow the specific wavelength topass to the sensor and block other wavelengths during the particularinterval. Additionally the switching interval of the filtering may belonger than the particular interval of light sources to account for thetravel time of the light to the sensor.

Body Tracking

FIG. 15 is a diagram depicting body tracking with a DVS according toaspects of the present disclosure. As shown a user 1501 may wear aheadset 1504 having a DVS 1503. Here, the DVS is depicted with twoarrays or DVS units or a camera and a DVS. The user 1501 is holding twocontrollers 1502 having two or more light sources 1505. The DVS 1503 hasthe controllers 1502 with their corresponding light sources 1505 withinits field of view (FOV). Additionally, the DVS 1503 may have appendagesof the user such as the user's hands or arms 1507, or legs or feet 1508within its FOV. The DVS may also have the ground or other landmarks 1509in its FOV. For example, and without limitation the light sensitiveelements of the DVS may detect reflections of light corresponding to theuser's appendages or the ground or other landmarks when the user movesor the light changes. The camera detects light reflections from thefield of view at the camera's frame rate. From the detected lightreflections, the user's appendages or the ground or landmarks may bedetermined 1506. In some alternative implementation an external DVS 1510may be used to track the user's appendages. The external DVS 1510 may bea distance away from the user chosen such that the user's appendages fitwithin the field of view of the external DVS 1510. For example andwithout limitation the external DVS may be located on top of orunderneath a television or computer monitor or other free-standing orwall mounted display.

A machine learning algorithm trained to determine the user's body,appendages, the ground or landmarks and their relative position andorientation from data, such as events or frames. The machine learningalgorithm may be a neural network and training may be similar to themethod discussed in the general neural network training section in FIGS.10A-10D above. A training set including labeled events or frames, orboth may be used to train a neural network. The labeled events or framesor both may include for example and without limitation the labels forthe user's body, appendages, the ground, landmarks, and relativeposition and orientation of the user's body, appendages, the ground, andlandmarks. Determination of the user's body, appendages, the ground orlandmarks and their relative position and orientation maybe performed inaddition to the position and orientation determination of the controller1502 e.g., using SLAM. Alternatively, the position and orientation ofthe controller may be determined with the same trained neural networkthat determines labels for the user's body, appendages, the ground orlandmarks and their relative position and orientation. As shown byelement 1506, a model may be fit to the determined user's body andappendages to improve the determination of relative position andlocation.

Safety Shutter

Body tracking in conjunction with determination of controller positionand orientation as discussed above may be used to trigger a safetyshutter in a VR or AR headset. FIG. 16A depicts a headset with a safetyshutter door according to aspects of the present disclosure. The headset1601 may include head strap 1604, an eyepiece 1603 and a display screen1602. The eyepiece 1603 may include one or more lenses configured tofocus on the display screen 1602. The one or more lenses may be forexample Fresnel lenses or prescription lenses. The display screen 1602may be transparent, a hole 1607 in the body of the headset may allowvision through the display screen 1602 when the safety shutter is open.

In this implementation, the safety shutter 1606 is a door that swingsaway from the hole 1607 when the safety system is activated. Here, asystem operated clasp 1605 interacts with a clasp 1608 on the safetyshutter door 1606 to secure the door closed over the hole 1607. Aspring-loaded hinge 1609 may ensure that safety shutter door 1606 opensquickly when the system operated clasp 1605 opens. The spring-loadedhinge may for example and without limitation have a clock-type springwound around hinge and secured to the door, the spring is wound when thedoor is closed and unwinds when the door opens. Alternatively, a flatspring may push against the safety shutter door 1606 when the door isclosed.

The system operated clasp 1605 may be configured to open the clasp whenthe safety system is activated. The safety system operated clasp 1605may include an electric motor or linear actuator that moves the clasp.The safety system may activate when the ground or one or more landmarksare detected near the system, user, user's body, or an appendage of theuser. The safety system may use the determination of the user's body,appendages, the ground or landmarks and their relative position andorientation as discussed above. Upon activation of the safety system asignal may be sent to the safety system operated clasp 1605 to open theclasp. When the clasp opens the spring-loaded hinge 1609 pushes thesafety shutter door 1606 open allowing the user to see through thedisplay 1602 and avoid the danger that set off the safety system.

Other safety shutter implementations may be used. For Example, FIG. 16Bshows an alternative headset with a sliding safety shutter according toaspects of the present disclosure. Here the safety shutter slide 1616slides out of the way of the hole 1607 when the safety system isactivated. The safety shutter slide 1616 may run on spring-loaded rails1619. Alternatively, the sliding safety shutter 1616 may include a tabthat runs in a slot 1619 in the body of the headset, a spring also inthe slot may push against the sliding safety shutter. In some additionalalternative implementations, the spring may be omitted, and gravity mayoperate the sliding safety shutter 1616. The spring-loaded rails 1619may push against sliding safety shutter 1616 when the shutter is closedand ensure that the safety shutter quickly opens when the safety systemis activated. The system operated clasp 1605 interacts with a clasp 1618on the sliding safety shutter 1616 to secure the slide closed.

The safety system may activate when the ground or one or more landmarksare detected near the system, user, user's body, or an appendage of theuser. The safety system may use the determination of the user's body,appendages, the ground or landmarks and their relative position andorientation as discussed above. Upon activation of the safety system asignal may be sent to system operated clasp 1605 to open the clasp. Whenthe clasp opens the spring 1619 pushes the sliding safety shutter 1616open allowing the user to see through the display 1602 and avoid thedanger that set off the safety system.

FIG. 16C depicts a headset with a louvered safety shutter according toaspects of the present disclosure. In this implementation the slats ofthe louvered safety shutter 1626 are longer in a first dimension than ina second dimension. In the closed position, the longer dimension of theslats is roughly parallel with the optics and each slat overlaps eitheranother slat or the headset body, blocking light through the displayscreen 1602. In the open position the slats change position such thatthe shorter dimension is parallel to the optics 1603 allowing light topass through the slats to the display screen. An actuator rod 1628 mayconnect each of the slats 1626 with a hinge. A safety-system controlledactuator 1629 may push or pull the actuator rod 1628 to open thelouvered safety shutter when the safety system is activated. In someother implementations the safety system-controlled actuator may bespring loaded, a clasp connected to the actuator rod 1628 and the systemcontrolled actuator 1629 may include a clasp that interfaces with theclasp of the actuator rod. The clasp of the system-controlled actuatormay open when the safety system is activated allowing the actuator rodto move under spring pressure, opening the louvers.

The safety system may activate when the ground or one or more landmarksare detected near the system, user, user's body, or an appendage of theuser. The safety system may use the determination of the user's body,appendages, the ground or landmarks and their relative position andorientation as discussed above. Upon activation a signal may be sent tosystem operated actuator to move the actuator rods. When the actuatorrod 1629 moves, it pushes the slats of the louvered safety shutter 1626open allowing the user to see through the display 1602 and avoid thedanger that set off the safety system.

FIG. 16D shows a fabric safety shutter according to aspects of thepresent disclosure. In this implementation the safety shutter 1636 iscomposed of an opaque fabric, for example and without limitation,tightly woven cotton fabric, polyester, vinyl, or tightly woven woolfabric. The fabric safety shutter 1636 may be coupled to a fabric roller1639. The fabric roller 1639 may be configured to roll up the fabricshutter when the safety system is activated. The fabric roller may befor example and without limitation spring loaded using a clock springsuch that when the safety shutter is closed the clock spring is undertension, alternatively an electric motor may be used to roll up thefabric safety shutter. A system operated clasp 1605 may interface with aclasp 1638 coupled to the fabric safety shutter 1636 and ensure that thefabric safety shutter does not unintentionally open.

During operation, the fabric safety shutter may be in a closed position.The safety system may activate when the ground or one or more landmarksare detected near the system, user, user's body, or an appendage of theuser. The safety system may use the determination of the user's body,appendages, the ground or landmarks and their relative position andorientation as discussed above. Upon activation of the safety system asignal may be sent to system operated clasp 1605 to open the clasp. Whenthe clasp opens the fabric roller 1619, rolls up the fabric safetyshutter 1616 allowing the user to see through the display 1602 and avoidthe danger that set off the safety system.

FIG. 16E shows a headset with a liquid crystal safety shutter accordingto aspects of the present disclosure. In this implementation a liquidcrystal screen 1646 is integrated into the headset 1601 otherwisecovering a hole in the headset. A safety system controlled liquidcrystal screen driver 1649 is communicatively coupled to the liquidcrystal screen 1646.

The liquid crystal screen 1646 may be for example and without limitationa liquid crystal shutter having a first light polarizer and a secondlight polarizer wherein the first polarizer has a 90-degree lightpolarization difference from the second light polarizer and a fluidfilled cavity. The fluid filled cavity may include liquid crystals thatare configured to have a first orientation in the absence of an electricfield that changes light polarization allowing light to pass from thefirst light polarizer through the second light polarizer. The liquidcrystals may be further configured to align in a second orientationunder an electric field. The second orientation of the liquid crystalsdo not change the polarization of light thus the light that passesthrough the first light polarizer is blocked at the second lightpolarizer. Electrodes may be disposed along a surface of the liquidfilled cavity allowing control of the liquid crystals. The safety systemcontrolled liquid crystal screen driver may be communicatively coupledwith the electrodes allowing control of the liquid crystals in the fluidfilled cavity. As used herein communicatively coupled means capable ofsending and/or receiving electric signals representing a message orinstructions from one coupled element to the other coupled element, thesignals may travel through intermediary elements and their format maychange but the message contained therein remains unchanged.

During operation, the safety system-controlled driver 1649 may sendsignals to the liquid crystal screen 1646 causing the liquid crystalsafety shutter 1646 to go opaque while the display screen 1602 isactive. When the safety system is activated the driver 1649 may causethe liquid crystal safety shutter 1646 to go transparent. For example,and without limitation, the driver may reduce voltage supplied to theliquid crystal safety shutter, returning the liquid crystals to theirfirst orientation which causes a change in the polarization of lightallowing light to pass through the second light polarizer. The safetysystem may activate when the ground or one or more landmarks aredetected near the system, user, user's body, or an appendage of theuser. The safety system may use the determination of the user's body,appendages, the ground or landmarks and their relative position andorientation as discussed above.

Finger Position Tracking

Aspects of the present disclosure may be applied to finger tracking.FIG. 17 depicts finger tracking with a DVS and controller according toaspects of the present disclosure. Here a controller 1701 includes twoor more light sources and one or more buttons 1705. The two or morelight sources include one or more light sources 1706 proximate to theone or more buttons 1705 and two or more other tracking light sources1703. As discussed above the two or more other tracking light sources1703 may generate events at the DVS 1702 that are used to determine theposition and orientation of the controller 1701. Shown here two DVS 1702or a DVS with two arrays and three other tracking light sources 1703 areused for determination of the position and orientation of thecontroller.

The one or more light sources 1706 proximate to the one or more buttons1705 may be used for finger tracking. For example and withoutlimitation, finger tracking may be accomplished with the DVS 1702 usingocclusion of the one or more light sources 1706 proximate to the buttons1705. The one or more light sources 1706 proximate to the buttons mayturn off and on at a predetermined interval. The DVS 1702 may generatean event with each flash. The events may be analyzed to determineocclusion of the one or more light sources 1706 proximate to the buttons1705. The configuration of the one or more light sources proximate tothe buttons may be known and thus when a light source is occluded by forexample and without limitation a finger or palm, the pattern of lightdetected in events generated by the DVS is different than when the lightsource is not occluded. As discussed above with respect to determinationof the position and orientation of the controller, here the timing ofthe flashes may be used to determine which lights are occluded andtherefore determine corresponding finger or palm position. When a lightsource 1706 proximate to a button 1705 has a reduced intensity or nointensity during the interval the light sources proximate to the buttonsare known to be ‘on’. That light source is determined to be occluded.Similarly, when a light known to be ‘on’ changes in detected intensity;an event may be generated and from the event it may be determined thatthe user's finger or hand has moved and the button has becomeunconcluded. The occlusion of one or more of the light sources proximateto the one or more buttons may be correlated to finger or palm positionsbased on their location. For example and without limitation, a lightsource located near the palm of the user when the controller 1701 isheld may be used to determine the location of the user's hand 1704.

The light sources 1706 may be located around each button 1705 and thebutton configuration of the controller and the design may be used todetermine finger position. For example and without limitation, thecontroller 1701 may be designed such that, when held, each finger of theuser is position near a button 1705. The pattern of light sourceocclusion determined from DVS events may then be used to determine whena user's finger is hovering over a button that has not been activatedand also may be used to determine when a user's finger has moved past abutton. This may be useful to provide further interaction options forusers, such as having a half button press or semi press or other buttonoptions. Multiple light sources may surround each button allowing for arefined determination of the position of the user's fingers or palm. Forexample, and without limitation in some implementations, ten or morelight sources may surround each button, in other implementations asingle light source may shine light through a translucent diffuseraround the button and the interruption in the diffuse light profile maybe used to determine finger position.

In some implementations, the button itself 1705 may also be a lightsource. The one or more buttons 1705 may turn off and on at a differentinterval than the one or more light sources proximate 1706 to the buttonor the other light tracking light sources 1703. Alternatively, the lightsource of the button 1705 may have a different wavelength orpolarization than the one or more light sources proximate 1706 or theother light tracking light sources 1703.

The buttons and tracking may also be used to enable a power saving modefor the light sources. For example, when a button is determined to bepressed, the one or more light sources proximate to that button may bedimmed or turned off. Additionally, if the controller 1701 is determinedto be out of view of the DVS 1702, the one or more lights proximate tothe buttons may be dimmed or turned off. In some implementation Datafrom the IMU may be used to determine if the controller is being held bya user for example and without limitation, if a change in an IMU datasuch as acceleration, angular rate etc. is not detected for a thresholdperiod of time then the light sources may be dimmed or turned off. Oncea change in IMU data is detected the light sources may be turned backon.

In an alternative implementation finger tracking may be performedwithout the use of one or more light sources. A machine learning modelmay be trained with a machine learning algorithm to detect fingerposition from events generated from ambient light changes due to fingermovement. The machine learning model may be a general machine learningmodel such as a CNN, RNN or DNN as discussed above. In someimplementations specialized machine learning model such as for exampleand without limitation a spiking (or sparking) neural network (SNN) maybe trained with a specialized machine learning algorithm. An SNN mimicsbiological NNs by having an activation threshold and a weight that isadjusted according to a relative spike time within an interval, alsoknown as Spike-timing-dependent-plasticity (STDP). When the activationthreshold is achieved the SNN is said to spike and transmit its weightto the next layer. An SNN may be trained via STDP and supervised orunsupervised learning techniques. and More information about SNNs can befound in Tavanaei, Amirhossein et al. “Deep Learning in Spiking NeuralNetworks” Neural Networks (2018) arXiv:1804.08150, the contents of whichare incorporated herein by reference for all purposes.

Alternatively, a high dynamic range (HDR) image may be constructed usingaggregated events from ambient data. A machine learning model trained torecognize hand position or controller position and orientation from HDRimages. The trained machine learning model may be applied to HDR imagesgenerated from the events to determine the hand/finger position orcontroller position and orientation. The machine learning model may be ageneral machine learning model trained with supervised learningtechniques as discussed in the general neural network training section.

Eye Tracking

Aspects of the present disclosure may be applied to eye tracking.Generally, eye tracking image analysis takes advantage ofcharacteristics distinctive to how light is reflected off of the eyes todetermine eye gaze direction from the image. For example, the image maybe analyzed to identify eye location based on corneal reflections in theimage data, and the image may be further analyzed to determine gazedirection based on a relative location of the pupils in the image.

Two common gaze tracking techniques for determining eye gaze directionbased on pupil location are known as Bright Pupil tracking and DarkPupil tracking. Bright Pupil tracking involves illumination of the eyeswith a light source that is substantially in line with the optical axisof the DVS, causing the emitted light to be reflected off of the retinaand back to the DVS through the pupil. The pupil presents in the imageas an identifiable bright spot at the location of the pupil, similar tothe red eye effect which occurs in images during conventional flashphotography. In this method of gaze tracking, the bright reflection frompupil itself helps the system locate the pupil if contrast between pupiland iris is not enough.

Dark Pupil tracking involves illumination with a light source that issubstantially offline from the optical axis of the DVS, causing lightdirected through the pupil to be reflected away from the optical axis ofthe DVS, resulting in an identifiable dark spot in the Event at thelocation of the pupil. In alternative Dark Pupil tracking systems, aninfrared light source and cameras directed at eyes can look at cornealreflections. Such DVS based systems track the location of the pupil andcorneal reflections which provides parallax due to different depths ofreflections gives additional accuracy.

FIG. 18A depicts an example of a dark pupil gaze tracking system 1800that may be used in the context of the present disclosure. The gazetracking system tracks the orientation of a user's eye E relative to adisplay screen 1801 on which visible images are presented. While adisplay screen is utilized in the example system of FIG. 18A, certainalternative embodiments may utilize an image projection system capableof projecting images directly into the eyes of a user. In theseembodiments, the user's eye E would be tracked relative to the imagesprojected into the user's eyes. In the example of FIG. 18A, the eye Egathers light from the screen 1801 through a variable iris I and a lensL projects an image on the retina R. The opening in the iris is known asthe pupil. Muscles control rotation of the eye E in response to nerveimpulses from the brain. Upper and lower eyelid muscles ULM, LLMrespectively control upper and lower eyelids UL LL in response to othernerve impulses.

Light sensitive cells on the retina R generate electrical impulses thatare sent to the user's brain (not shown) via the optic nerve ON. Thevisual cortex of the brain interprets the impulses. Not all portions ofthe retina R are equally sensitive to light. Specifically,light-sensitive cells are concentrated in an area known as the fovea.

The illustrated image tracking system includes one or more infraredlight sources 1802, e.g., light emitting diodes (LEDs) that directnon-visible light (e.g., infrared light) toward the eye E. Part of thenon-visible light reflects from the cornea C of the eye and partreflects from the iris. The reflected non-visible light is directedtoward a DVS 1804 sensitive to infrared light by a wavelength-selectivemirror 1806. The mirror transmits visible light from the screen 1801 butreflects the non-visible light reflected from the eye.

The DVS 1804 produces an event of the eye E which may be analyzed todetermine a gaze direction GD from the relative position of the pupil.This event may be produced with a processor 1805. The DVS 1804 isadvantageous in this implementation as the extremely fast update ratefor events provides near real time information on changes in the user'sgaze.

As seen in FIG. 18B, the event 1811 showing a user's head H may beanalyzed to determine a gaze direction GD from the relative position ofthe pupil. For example, analysis may determine a 2-dimensional offset ofthe pupil P from a center of the eye E in the image. The location of thepupil relative to the center may be converted to a gaze directionrelative to the screen 1801, by a straightforward geometric computationof a three-dimensional vector based on the known size and shape of theeyeball. The determined gaze direction GD is capable of showing therotation and acceleration of the eye E as it moves relative to thescreen 1801.

As also seen in FIG. 18B, the event may also include reflections 1807and 1808 of the non-visible light from the cornea C and the lens L,respectively. Since the cornea and lens are at different depths, theparallax and refractive index between the reflections may be used toprovide additional accuracy in determining the gaze direction GD. Anexample of this type of eye tracking system is a dual Purkinje tracker,wherein the corneal reflection is the 1st Purkinje Image, and the lensreflection is the 4th Purkinje Image. There may also be reflections 1808from a user's eyeglasses 1809, if these are worn a user.

Performance of eye tracking systems depend on a multitude of factors,including the placement of light sources (IR, visible, etc.) and DVS,whether user is wearing glasses or contacts, Headset optics, trackingsystem latency, rate of eye movement, shape of eye (which changes duringthe course of the day or can change as a result of movement), eyeconditions, e.g., lazy eye, gaze stability, fixation on moving objects,scene being presented to user, and user head motion. The DVS provides anextremely fast update rate for events with reduced extraneousinformation output to the processor. This allows for quicker processingand faster gaze tracking state and error parameter determination.

Error parameters that may be determined from gaze tracking data mayinclude, but are not limited to, rotation velocity and prediction error,error in fixation, confidence interval regarding the current and/orfuture gaze position, and errors in smooth pursuit. State informationregarding a user's gaze involves the discrete state of the user's eyesand/or gaze. Accordingly, example state parameters that may bedetermined from gaze tracking data may include, but are not limited to,blink metrics, saccade metrics, depth of field response, colorblindness, gaze stability, and eye movement as a precursor to headmovement.

In certain implementations, the gaze tracking error parameters caninclude a confidence interval regarding the current gaze position. Theconfidence interval can be determined by examining the rotationalvelocity and acceleration of a user's eye for change from last position.In alternative embodiments, the gaze tracking error and/or stateparameters can include a prediction of future gaze position. The futuregaze position can be determined by examining the rotational velocity andacceleration of eye and extrapolating the possible future positions ofthe user's eye. In general terms, the DVS update rate of the gazetracking system may lead to a small error between the determined futureposition and the actual future position for a user with larger values ofrotational velocity and acceleration because the updated rate of the DVSis so high this small error may be significantly less than existingcamera based systems.

In yet further alternative implementations, the gaze tracking errorparameters can include a measurement of the eye speed, e.g., therotation rate. In certain alternative embodiments, the determined gazetracking state parameters include measuring the metrics of a user'sblink. During a typical blink, a period of 150 milliseconds (ms)typically elapses wherein a user's vision is not focused on thepresented images. Thus, depending on the frame rate of the displaydevice, a user's vision may not be focused on the presented images forup to 20-30 frames. However, upon exiting the blink, the user's gazedirection may not correspond to the last measured gaze direction asdetermined by the obtained gaze tracking data. Accordingly, metrics of auser's gaze may be determined from the obtained gaze tracking data.These metrics may include, but are not limited to, the measured startand end times of the blink of a user as well as the predicted end times.

In yet additional alternative implementations, the determined gazetracking state parameters include measuring the metrics of a user'ssaccades. During a typical saccade, a period of 20-200 ms typicallyelapses wherein a user's vision is not focused on the presented images.Thus, depending on the frame rate of the display device, a user's visionmay not be focused on the presented images for anywhere up to 40 frames.However, as a result of the nature of a saccade, the user's gazedirection will have shifted to a different region of interest when thesaccade is exited. Accordingly, gaze tracking data may be used inestablishing the metrics of a user's saccade based on the actual orpredicted time that will elapse during the saccade. These metrics mayinclude, but are not limited to, the measured start and end times of thesaccades of a user as well as the predicted end times.

In certain alternative implementations, the determined gaze trackingstate parameters include determining a transition in the gaze directionof a user between areas of interest as a result of a change in depth offield between presented images. Because providing a transition betweenareas of interest in presented images will result in the user undergoinga saccade.

In yet additional alternative implementations, the determined gazetracking state parameters may adapt for color blindness. For example,regions of interest may be present in an image presented to a user suchthat the regions would not be noticeable by a user who has a particularform of color blindness. The gaze tracking data obtained at woulddetermine whether or not the user's gaze identified or responded to thearea of interest, for example, as a result of the user's changed gazedirection. Accordingly, it may be determined, as a gaze tracking errorparameter, whether or not a user is color blind to a particular color orspectrum.

In certain alternative implementations, the determined gaze trackingstate parameters include a measurement of the gaze stability of a user.Determining gaze stability may be performed by measuring themicrosaccadic radius of the user's eye; smaller fixation overshoot andundershoot equates to a more stable gaze in a user.

In yet additional alternative implementations, the determined gazetracking error and/or state parameters include a user's ability tofixate on moving objects. These parameters may include the measurementof the capability of a user's eye to undergo smooth pursuit and themaximum object pursuit speed of the eyeball. Typically, a user withexcellent smooth pursuit capabilities experiences less jitter in themovement of the eyeball.

In certain alternative implementations, the determined gaze trackingerror and/or state parameters include a determination of eye movement asa precursor to head movement. Offset between head and eye orientationcan affect certain error and/or state parameters as discussed above,e.g., in smooth pursuit or fixation.

More information regarding gaze tracking and error parameterdetermination may be found in U.S. Pat. No. 10,192,528 the contents ofwhich are incorporated by reference herein for all purposes.

System

FIG. 19 is a block system diagram for a system for tracking with a DVSaccording to aspects of the present disclosure. By way of example, andnot by way of limitation, according to aspects of the presentdisclosure, the system 1900 may be an embedded system, mobile phone,personal computer, tablet computer, portable game device, workstation,game console, and the like.

The system 1900 generally includes a central processor unit (CPU) 1903,and a memory 1904. The system 1900 may also include well-known supportfunctions 1906, which may communicate with other components of thesystem, e.g., via a data bus 1905. Such support functions may include,but are not limited to, input/output (I/O) elements 1907, power supplies(P/S) 1911, a clock (CLK) 1912 and cache 1913.

The system 1900 may include a display device 1931 to present renderedgraphics to a user. In alternative implementations, the display deviceis a separate component that works in conjunction with the system, 1900.The display device 1931 may be in the form of a flat panel display, headmounted display (HMD), cathode ray tube (CRT) screen, projector, orother device that can display visible text, numerals, graphical symbols,or images.

Here, the display device 1931 is coupled with a DVS 1901A and acontroller 1902 includes two or more light sources 1932A, which may bein any of the configurations described herein. In alternativeimplementations, the DVS may be coupled to the game controller and thedisplay device may include two or more light sources instead. In yetother alternative implementations, the DVS is a separate unit uncoupledfrom either the display device or the controller, the controller anddisplay device in this case may both include two or more light sourcesfor tracking.

In some implementations, e.g., where the display device is part of ahead-mounted display (HMD), such HMD may include an inertial measurementunit (IMU), such as an accelerometer or gyroscope. As also discussedhereinabove, such an HMD may include light sources 1932B that may betracked using a DVS that is separate from the display device 1901 andcoupled to the CPU 1903. By way of example, a separate DVS 1901B may bemounted to the controller 1902.

In some implementations, the DVS 1901A or DVS 1901B may be part of ahybrid sensor, e.g., as discussed above with respect to FIG. 11A, FIG.11B, FIG. 11C, or FIG. 11D. Such a hybrid sensor may include a depthsensor, e.g., a DTOF sensor, in which case the hybrid sensor may includean illumination unit (not shown).

Furthermore, where the display device 1931 is part of an HMD, the devicemay be fitted with an optional safety shutter 1933, which may beoperably coupled to a processor, such as the CPU 1903, and operate asdiscussed above with respect to FIG. 16A, FIG. 16B, FIG. 16C, FIG. 16Dor FIG. 16E. Alternatively, the safety shutter may be controlled by aseparate processor mounted to the HMD.

The system 1900 includes a mass storage device 1915 such as a diskdrive, CD-ROM drive, flash memory, solid state drive (SSD), tape drive,or the like to provide non-volatile storage for programs and/or data.The system 1900 may also optionally include a user interface unit 1916to facilitate interaction between the system 1900 and a user. The userinterface 1916 may include a keyboard, mouse, joystick, light pen, orother device that may be used in conjunction with a graphical userinterface (GUI). The system 1900 may also include a network interface1914 to enable the device to communicate with other devices over anetwork 1920. The network 1920 may be, e.g., a local area network (LAN),a wide area network such as the internet, a personal area network, suchas a Bluetooth network or other type of network. These components may beimplemented in hardware, software, or firmware, or some combination oftwo or more of these.

The CPU 1903 may each include one or more processor cores, e.g., asingle core, two cores, four cores, eight cores, or more. In someimplementations, the CPU 1903 may include a GPU core or multiple coresof the same Accelerated Processing Unit (APU). The memory 1904 may be inthe form of an integrated circuit that provides addressable memory,e.g., random access memory (RAM), dynamic random-access memory (DRAM),synchronous dynamic random access memory (SDRAM), and the like. The mainmemory 1904 may include application data 1923 used by the processor 1903while processing. The main memory 1904 may also include event data 1909received from the DVS 1901. A trained Neural Network (NN) 1910 may beloaded into Memory 1904 for determination of position and orientationdata as discussed in FIG. 9 . Additionally, the Memory 1904 may includemachine learning algorithms 1921 for training or adjusting the NN 1910.A database 1922 may be included in the memory 1904. The database maycontain information about the light source configurations, predeterminedflash intervals of each one or more light sources, and the like. Thememory may also contain outputs from IMUs coupled to the controller 1902or display device 1931. In some implementations the memory 1904 maycontain outputs from the one or more light sources such time stamps wheneach of the light sources are on or off.

According to aspects of the present disclosure the processor 1903 maycarry out methods for determining the position and orientation of acontroller or user as discussed in FIGS. 8 and 9 , these methods may beloaded into memory 1904 as applications 1923. The processor may generateone or more orientations and configuration of the controller, headset,user's body or appendages, the ground or a landmark as a result ofcarrying out the methods described in FIGS. 8 and 9 and furtherdescribed with respect to FIG. 15 . These positions and orientations maybe held in the database 1922 and may be used in successive iteration ofthe methods of FIGS. 8 and 9 . In some implementations, the processor1903 may utilize such positions and/or orientations in a machinelearning algorithm trained to perform simultaneous localization andmapping (SLAM).

The Mass Storage 1915 may contain Application or Programs 1917 that areloaded to the main memory 1904 when processing begins on the application1923. Additionally, the mass storage 1915 may contain data 1918 used bythe processor during processing of applications 1923, NN 1910, machinelearning algorithms 1921 and filling the database 1922.

As used herein and as is generally understood by those skilled in theart, an application-specific integrated circuit (ASIC) is an integratedcircuit customized for a particular use, rather than intended forgeneral-purpose use.

As used herein and as is generally understood by those skilled in theart, a Field Programmable Gate Array (FPGA) is an integrated circuitdesigned to be configured by a customer or a designer aftermanufacturing—hence “field-programmable”. The FPGA configuration isgenerally specified using a hardware description language (HDL), similarto that used for an ASIC.

As used herein and as is generally understood by those skilled in theart, a system on a chip or system on chip (SoC or SOC) is an integratedcircuit (IC) that integrates all components of a computer or otherelectronic system into a single chip. It may contain digital, analog,mixed-signal, and often radio-frequency functions—all on a single chipsubstrate. A typical application is in the area of embedded systems.

A typical SoC includes the following hardware components:

-   -   One or more processor cores (e.g., microcontroller,        microprocessor, or digital signal processor (DSP) cores.    -   Memory blocks, e.g., read only memory (ROM), random access        memory (RAM), electrically erasable programmable read-only        memory (EEPROM) and flash memory.    -   Timing sources, such as oscillators or phase-locked loops.    -   Peripherals, such as counter-timers, real-time timers, or        power-on reset generators.    -   External interfaces, e.g., industry standards such as universal        serial bus (USB), FireWire, Ethernet, universal asynchronous        receiver/transmitter (USART), serial peripheral interface (SPI)        bus.    -   Analog interfaces including analog to digital converters (ADCs)        and digital to analog converters (DACs).    -   Voltage regulators and power management circuits.

These components are connected by either a proprietary orindustry-standard bus. Direct Memory Access (DMA) controllers route datadirectly between external interfaces and memory, bypassing the processorcore and thereby increasing the data throughput of the SoC.

A typical SoC includes both the hardware components described above, andexecutable instructions (e.g., software or firmware) that controls theprocessor core(s), peripherals, and interfaces.

Aspects of the present disclosure provide for image-based trackingcharacterized by a higher sample rate than is possible with conventionalimage-based tracking systems thereby leading to improved fidelity oftracking. Additional advantages include reduced cost, reduced weight,reduced generation of extraneous data, and reduced processingrequirements when using a DVS-based tracking system. Such advantagesallow for improved Virtual Reality (VR) and Augmented Reality (AR)systems, among other applications.

While the above is a complete description of the preferred embodiment ofthe present invention, it is possible to use various alternatives,modifications, and equivalents. Therefore, the scope of the presentinvention should be determined not with reference to the abovedescription but should, instead, be determined with reference to theappended claims, along with their full scope of equivalents. Any featuredescribed herein, whether preferred or not, may be combined with anyother feature described herein, whether preferred or not. In the claimsthat follow, the indefinite article “A”, or “An” refers to a quantity ofone or more of the items following the article, except where expresslystated otherwise. The appended claims are not to be interpreted asincluding means-plus-function limitations, unless such a limitation isexplicitly recited in a given claim using the phrase “means for.”

What is claimed is:
 1. A tracking method, comprising: determining aposition and orientation of a controller from a known configuration oftwo or more light sources with respect to each other and with respect toa controller body and from output signals from a dynamic vision sensor(DVS) generated in response to changes in light output from the two ormore light sources, wherein the output signals include informationcorresponding to times of two or more events at two or morecorresponding light-sensitive elements in an array of two or morelight-sensitive elements in the DVS and locations of the two or morecorresponding light-sensitive elements in the array; and determining aposition and orientation of one or more objects from signals generatedby two or more light-sensitive elements resulting from other lightreaching the two or more light-sensitive elements.
 2. The trackingmethod of claim 1, wherein the one or more objects include thecontroller.
 3. The tracking method of claim 1, wherein the one or moreobjects include one or more objects other than the controller.
 4. Thetracking method of claim 1, wherein the one or more objects include thecontroller and one or more objects other than the controller.
 5. Thetracking method of claim 1, wherein determining a position andorientation of one or more objects from signals generated by two or morelight-sensitive elements resulting from other light reaching the two ormore light-sensitive elements includes determining a map of anenvironment and a state of the one or more objects.
 6. The trackingmethod of claim 1, further comprising triggering an optical safetyshutter on a headset in response to the determined position of the oneor more objects.
 7. The tracking method of claim 6 wherein the opticalsafety shutter includes an opaque blind operably coupled to the headset,wherein the opaque blind is configured to selectively allow light to bepassed to an eye of a user wearing the headset.
 8. The tracking methodof claim 7 wherein triggering the optical safety shutter includestriggering the optical safety shutter to change from an opaque state toa light-transmitting state in response to the determined position of theone or more objects.
 9. The tracking method of claim 8 where in theoptical safety shutter includes a liquid crystal shutter.
 10. Thetracking method of claim 1, wherein determining a position andorientation of one or more objects from signals generated by two or morelight-sensitive elements resulting from other light reaching the two ormore light-sensitive elements includes determining a map of anenvironment and a state of the one or more objects from signalsgenerated by two or more light-sensitive elements in a DVS located onthe controller.
 11. The tracking method of claim 1, wherein determininga position and orientation of one or more objects includes using amachine learning algorithm to fit a position and orientation of the oneor more objects to the signals generated by the two or more lightsensitive elements.
 12. The tracking method of claim 1 furthercomprising receiving information corresponding to an angular rate ofchange or specific force or specific acceleration or a change inmagnetic flux from an Inertial Measurement Unit (IMU).
 13. The trackingmethod of claim 12 wherein determining the position and orientation ofthe controller further includes using the information corresponding tothe angular rate of change or specific force or specific acceleration orchange in magnetic flux in determining the position and orientation ofthe controller.
 14. The tracking method of claim 13 further comprisingusing a Kalman Filter to fuse angular rate of change or specificforce/acceleration or a change in magnetic flux information with the twoor more events.
 15. The tracking method of claim 1, wherein determiningthe position and orientation of the controller includes using thechanges in light output and a known pattern of changes in light outputto determine a location of at least one of the two or more light sourcesin the known configuration.
 16. The tracking method of claim 1 furthercomprising notifying a user of the location of the one or more objectsbased on the determined position and orientation of the one or moreobjects.
 17. The tracking method of claim 1 further comprisingtriggering an optical safety shutter on a headset in response to thedetermined position of the controller.
 18. The tracking method of claim17 wherein the optical safety shutter includes an opaque blind operablycoupled to the headset and wherein the opaque blind is configured toselectively allow light to be passed to an eye of a user wearing theheadset.
 19. The tracking method of claim 17 wherein triggering theoptical safety shutter includes triggering the optical safety shutter togo from an opaque state to a light-transmitting state in response to thedetermined position of the one or more objects.
 20. The tracking methodof claim 19 where in the optical safety shutter includes a liquidcrystal shutter.